Research on semi-supervised community discovery algorithm based on new annealing
- Author(s): Jinghong Wang 1, 2, 3 ; Jiateng Yang 1, 2 ; Yichao He 4
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View affiliations
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Affiliations:
1:
College of Computer and Cyber Security, Hebei Normal University , Shijiazhuang City, Hebei Province , People's Republic of China ;
2: Hebei Key Lab Network and Information Security , Shijiazhuang City, Hebei Province , People's Republic of China ;
3: University of Illinois at Champaign , Urbana, Illinois 61801 , USA ;
4: School of Information Engineering, Hebei GEO University , Shijiazhuang Hebei 050031 , People's Republic of China
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Affiliations:
1:
College of Computer and Cyber Security, Hebei Normal University , Shijiazhuang City, Hebei Province , People's Republic of China ;
- Source:
Volume 2020, Issue 12,
December
2020,
p.
1149 – 1154
DOI: 10.1049/joe.2019.1186 , Online ISSN 2051-3305
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This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received
21/10/2019,
Accepted
19/11/2019,
Published
13/01/2020
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Inspec keywords: complex networks; iterative methods; graph theory; social network theory; network theory (graphs); optimisation
Other keywords: must-link constraints; similarity information; community detection methods; semisupervised GN algorithm; new annealing maximisation algorithm; cannot-link constraints; semisupervised community discovery algorithm; node similarity
Subjects: Interpolation and function approximation (numerical analysis); Combinatorial mathematics; Systems theory applications in social science and politics; Optimisation techniques
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